This thesis aims to provide new insights into the importance of decomposing aggregate time series data using the Maximum Overlap Discrete Wavelet Transform. In particular, the analysis throughout this thesis involves decomposing aggregate financial time series data at hand into approximation (low-frequency) and detail (high-frequency) components. Following this, information and hidden relations can be extracted for different investment horizons, as matched with the detail components. The first study examines the ability of different GARCH models to forecast stock return volatility in eight international stock markets. The results demonstrate that de-noising the returns improves the accuracy of volatility forecasts regardless of the statistical test employed. After de-noising, the asymmetric GARCH approach tends to be preferred, although that result is not universal. Furthermore, wavelet de-noising is found to be more important at the key 99% Value-at-Risk level compared to the 95% level. The second study examines the impact of fourteen macroeconomic news announcements on the stock and bond return dynamic correlation in the U.S. from the day of the announcement up to sixteen days afterwards. Results conducted over the full sample offer very little evidence that macroeconomic news announcements affect the stock-bond return dynamic correlation. However, after controlling for the financial crisis of 2007-2008 several announcements become significant both on the announcement day and afterwards. Furthermore, the study observes that news released early in the day, i.e. before 12 pm, and in the first half of the month, exhibit a slower effect on the dynamic correlation than those released later in the month or later in the day. While several announcements exhibit significance in the 2008 crisis period, only CPI and Housing Starts show significant and consistent effects on the correlation outside the 2001, 2008 and 2011 crises periods. The final study investigates whether recent returns and the time-scaled return can predict the subsequent trading in ten stock markets. The study finds little evidence that recent returns do predict the subsequent trading, though this predictability is observed more over the long-run horizon. The study also finds a statistical relation between trading and return over the long-time investment horizons of [8-16] and [16-32] day periods. Yet, this relation is mostly a negative one, only being positive for developing countries. It also tends to be economically stronger during bull-periods.

第一章 台灣與美國股市價量關係的分量迴歸分析 摘要 本文利用分量迴歸來觀察台灣和美國股市報酬率和成交量的價量關係。實證結果發現兩地股市的價量關係截然不同。台灣股市的報酬率與成交量之間具有正向關係，呈現「價量齊揚」和「價跌量縮」的現象，而前者效果通常較顯著；但報酬率接近最大漲幅限制時，報酬率與成交量之間並無顯著關係，報酬率接近最大跌幅限制時，「價跌量縮」的現象甚至更強。相對於台灣，美國股市的報酬率與成交量則出現「價量齊揚」與「價量背離」互相對稱的 “V” 字關係。就實證方法而言，傳統以 OLS 方法估計的迴歸模型並無法得到上述的實證結果。進一步的分析顯示，融券成數的高低以及平盤以下不得放空等規定都是造成台灣股市出現「價跌量縮」的可能原因。 第二章 股市價量關係的分量迴歸分析 (二) 摘要 本章利用分量迴歸觀察包括台灣在內的亞洲新興工業國家與成熟股市的價量關係。實證結果顯示，亞洲新興工業國家和日本股市「價量齊揚」的效果較強，其中香港、南韓和新加坡呈現較弱的「價量背離」現象，因此價量之間有不對稱的 “V” 字關係；而日本股市則呈現「價跌量縮」，與第一章分析的台灣股市價量關係相似。在成熟股市的價量關係中，英國金融時報指數、美國道瓊工業指數和德國股價指數皆呈現對稱的 “V” 字關係，與美國US指數的價量關係相似。亞洲地區的國家在1997下半年到1998上半年普遍經歷了一場金融風暴，本文進一步的分析發現在這場風暴期間，亞洲地區除了台灣以外，日本、香港、南韓與新加坡都出現較強的「價量齊揚」與「價量背離」，這種現象可能肇因於投資人認為風暴期間的股價報酬率風險較高，遂使得股價報酬率對成交量的反應較為敏銳。相對而言，歐美地區的國家，受到亞洲金融風暴的影響較小，所以整體的價量關係在亞洲金融風暴期間並無重大改變。本章的結果都是透過分量迴歸所獲得。 第三章 股市價量因果關係的分量迴歸分析 摘要 本文依據分量迴歸設計 Granger 因果關係的新檢驗方法，並依此方法來檢驗幾個股市價量之間的因果關係。本文分析的股市包括日本、英國與美國等世界前三大股市，以及合稱亞洲四小龍的台灣、香港、南韓與新加坡等新興工業國家或地區的股市。實證結果顯示：除了台灣股市以外，其他的股市皆呈現 “V” 字的跨期價量關係。其中英國、美國、香港和新加坡股市的跨期價量關係大體呈現正向「價量齊揚」與負向「價量背離」互相對稱的 “V” 字關係，而日本和南韓股市則是「價量齊揚」較強的不對稱 “V” 字關係。此一結果表示這些股市的價量之間都存在分配上的 Granger (1969) 因果關係。但若以均數迴歸來衡量跨期價量關係，則所有股市都呈現不顯著的跨期價量關係，也就是傳統文獻上所謂價量之間沒有 Granger 因果關係。本文所提出的 Granger 因果關係之分量迴歸分析，可以觀察到整個條件分配中各分量的因果關係，為分配上的 Granger 因果關係提供一個較完整的檢驗方法。 / We examine the relationship between the stock return and trading volume in the Taiwan and U.S. Stock Exchanges using quantile regression. The empirical results show that the return-volume relations in these two exchanges are quite different. For Taiwan data, there are significant positive return-volume relations across quantiles, showing that a large positive return is usually accompanied with a large trading volume and a large negative return with a small trading volume, yet the effect of former is stronger. However, such relations change when returns approach the price limits. We also find that for U.S. data, return-volume relations exhibit symmetric V-shapes across quantiles, showing that a large return (in either sign) is usually accompanied with a large trading volume. On the other hand, linear regressions estimated by the ordinary least square method are unable to reveal such patterns. Further investigation shows that various restrictions on short sales in the Taiwan Stock Exchange may explain the difference between the return-volume relations in Taiwan and U.S. data.